Merge pull request #4 from margarita0303/streaming_lm_algorithm

tests changed

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt

@@ -52,8 +52,8 @@ fun main() {
     val consts = BroadcastDoubleTensorAlgebra.fromArray(
         ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
     ).as2D()
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-2, 0.015, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
+    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
-//    val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-3, 11.0, 9.0, 1.0)
+//    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
 
     val result = DoubleTensorAlgebra.lm(
         ::funcDifficultForLm,

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt

@@ -12,6 +12,7 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcMiddleForLm
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
 import space.kscience.kmath.tensors.core.internal.LMSettings
 import kotlin.math.roundToInt
 fun main() {
@@ -52,7 +53,7 @@ fun main() {
     val consts = BroadcastDoubleTensorAlgebra.fromArray(
         ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
     ).as2D()
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
+    val opts = doubleArrayOf(3.0, 10000.0, 1e-3, 1e-3, 1e-3, 1e-3, 1e-15, 11.0, 9.0, 1.0)
 
     val result = DoubleTensorAlgebra.lm(
         ::funcMiddleForLm,
@@ -76,7 +77,7 @@ fun main() {
     }
     println()
 
-    println("Y true and y received:")
+
     var y_hat_after =  funcMiddleForLm(t_example, result.result_parameters, settings)
     for (i in 0 until y_hat.shape.component1()) {
         val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt

@@ -6,20 +6,27 @@
 package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
 
 import space.kscience.kmath.nd.*
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
+import space.kscience.kmath.tensors.LevenbergMarquardt.*
-import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
 import kotlin.math.roundToInt
 
 suspend fun main(){
-    val startData = getStartDataForFuncEasy()
+    val startData = getStartDataForFuncDifficult()
     // Создание потока:
-    val lmFlow = streamLm(::funcEasyForLm, startData, 1000,  10)
+    val lmFlow = streamLm(::funcDifficultForLm, startData, 0,  100)
+    var initialTime = System.currentTimeMillis()
+    var lastTime: Long
+    val launches = mutableListOf<Long>()
     // Запуск потока
     lmFlow.collect { parameters ->
+        lastTime = System.currentTimeMillis()
+        launches.add(lastTime - initialTime)
+        initialTime = lastTime
         for (i in 0 until parameters.shape.component1()) {
             val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
             print("$x ")
             if (i == parameters.shape.component1() - 1) println()
         }
     }
+
+    println("Average without first is: ${launches.subList(1, launches.size - 1).average()}")
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt

@@ -10,6 +10,7 @@ import space.kscience.kmath.nd.ShapeND
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.nd.component1
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
@@ -17,6 +18,7 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
 import space.kscience.kmath.tensors.core.asDoubleTensor
 import space.kscience.kmath.tensors.core.internal.LMSettings
+import kotlin.math.roundToInt
 
 public data class StartDataLm (
     var lm_matx_y_dat: MutableStructure2D<Double>,
@@ -88,6 +90,91 @@ fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
     return y_hat.as2D()
 }
 
+fun getStartDataForFuncDifficult(): StartDataLm  {
+    val NData = 200
+    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
+    for (i in 0 until NData) {
+        t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
+    }
+
+    val Nparams = 15
+    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_example[i, 0] = p_example[i, 0] + i - 25
+    }
+
+    val settings = LMSettings(0, 0, 1)
+
+    var y_hat =  funcDifficultForLm(t_example, p_example, settings)
+
+    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_init[i, 0] = (p_example[i, 0] + 0.9)
+    }
+
+    var t = t_example
+    val y_dat = y_hat
+    val weight = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 / Nparams * 1.0 - 0.085 }
+    ).as2D()
+    val dp = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
+    ).as2D()
+    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / -50.0)
+    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / 50.0)
+    val consts = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
+    ).as2D()
+    val opts = doubleArrayOf(3.0, 10000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
+
+    return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
+}
+
+fun getStartDataForFuncMiddle(): StartDataLm  {
+    val NData = 100
+    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
+    for (i in 0 until NData) {
+        t_example[i, 0] = t_example[i, 0] * (i + 1)
+    }
+
+    val Nparams = 20
+    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_example[i, 0] = p_example[i, 0] + i - 25
+    }
+
+    val settings = LMSettings(0, 0, 1)
+
+    var y_hat =  funcMiddleForLm(t_example, p_example, settings)
+
+    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_init[i, 0] = (p_example[i, 0] + 10.0)
+    }
+    var t = t_example
+    val y_dat = y_hat
+    val weight = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
+    ).as2D()
+    val dp = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
+    ).as2D()
+    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / -50.0)
+    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / 50.0)
+    val consts = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
+    ).as2D()
+    val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
+
+    var example_number = 1
+
+    return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
+}
+
 fun getStartDataForFuncEasy(): StartDataLm {
     val lm_matx_y_dat = doubleArrayOf(
         19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,

kmath-tensors/build.gradle.kts

@@ -7,7 +7,7 @@ kscience{
     js {
         browser {
             testTask {
-                useMocha().timeout = "100000"
+                useMocha().timeout = "0"
             }
         }
     }

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestLmAlgorithm.kt

@@ -245,7 +245,7 @@ class TestLmAlgorithm {
         val consts = BroadcastDoubleTensorAlgebra.fromArray(
             ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
         ).as2D()
-        val opts = doubleArrayOf(3.0, 7000.0, 1e-2, 1e-1, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
+        val opts = doubleArrayOf(3.0, 7000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
 
         val result = DoubleTensorAlgebra.lm(
             ::funcDifficultForLm,
@@ -261,7 +261,5 @@ class TestLmAlgorithm {
             10,
             1
         )
-
-//        assertEquals(1.15, (result.result_chi_sq * 1e2).roundToLong() / 1e2)
     }
 }